KGANCDA: predicting circRNA-disease associations based on knowledge graph attention network

Abstract Increasing evidences have proved that circRNA plays a significant role in the development of many diseases. In addition, many researches have shown that circRNA can be considered as the potential biomarker for clinical diagnosis and treatment of disease. Some computational methods have been...

Full description

Saved in:
Bibliographic Details
Published in:Briefings in bioinformatics 2022-01, Vol.23 (1)
Main Authors: Lan, Wei, Dong, Yi, Chen, Qingfeng, Zheng, Ruiqing, Liu, Jin, Pan, Yi, Chen, Yi-Ping Phoebe
Format: Article
Language:English
Subjects:
Biomarkers
Case studies
Computational Biology - methods
Computer applications
Disease
Knowledge representation
Medical treatment
MicroRNAs - genetics
miRNA
Multilayer perceptrons
Neural Networks, Computer
Pattern Recognition, Automated
RNA, Circular
Sparsity
Citations: Items that this one cites
Items that cite this one
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Abstract Increasing evidences have proved that circRNA plays a significant role in the development of many diseases. In addition, many researches have shown that circRNA can be considered as the potential biomarker for clinical diagnosis and treatment of disease. Some computational methods have been proposed to predict circRNA-disease associations. However, the performance of these methods is limited as the sparsity of low-order interaction information. In this paper, we propose a new computational method (KGANCDA) to predict circRNA-disease associations based on knowledge graph attention network. The circRNA-disease knowledge graphs are constructed by collecting multiple relationship data among circRNA, disease, miRNA and lncRNA. Then, the knowledge graph attention network is designed to obtain embeddings of each entity by distinguishing the importance of information from neighbors. Besides the low-order neighbor information, it can also capture high-order neighbor information from multisource associations, which alleviates the problem of data sparsity. Finally, the multilayer perceptron is applied to predict the affinity score of circRNA-disease associations based on the embeddings of circRNA and disease. The experiment results show that KGANCDA outperforms than other state-of-the-art methods in 5-fold cross validation. Furthermore, the case study demonstrates that KGANCDA is an effective tool to predict potential circRNA-disease associations.
ISSN:1467-5463
1477-4054
DOI:10.1093/bib/bbab494